Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2013-2014

C O N T E N TS
■ Message from Director-General ——

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■ Institute Overview—

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■ Research Centers——

4■ NOE ( Network Of Excellence) Project 14■ Project for Fostering andPromoting Statistical Thinking——
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■ Research Cooperation——
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■ Graduate School Program—
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■ Finance and Buildings——
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■ Organization——
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■ History—

Message from Director-General

Over the last financial year, which we should call the “First Year of Big
Data”, numerous seminars and symposiums relating to the utilization of“big data” were held across the spheres of industry, government, and academia. As a result, a large number of research and development projects
and plans are now underway. Of course, while some people leap instantly
at the latest, most popular topics, there will always be others who take acool and cautious attitude to new trends. Some people even take the cynical view that the season of big data has already passed and that big data
is now going rapidly downhill. This is probably inevitable in the case of
the media, obsessed as they are by the topic of the moment, but nonetheless the essential fact that big data will have a deep impact on societyneeds to be clearly recognized.
Up until 20 or 30 years ago, money and information circulated within the
structure of society in a rather gradual and steady way, somewhat similar
to such physical phenomena advection and diffusion. Now, however, due
to the mass penetration of the Internet, money and information move at high speed,
without any connection to real-world distances. As a result, the role previously
played by the constituents in a social structure has been lost, and many types ofwork and occupations are disappearing. Under this new structure, there are no firstprinciples (governing equations) that describe the phenomena arising under this new
structure, and the conveyance and processing of information—that is, computational
services— are generating huge amounts of economic value. In the business world, bigdata are the measurements of this social structure. Thus, instead of solving governing
equations, what has become important is to understand phenomena using clues provided by big data and to develop modeling techniques for enabling better predictions
and decision-making.
Anticipating the full-scale advent of the “big data era”, the Institute of Statistical
Mathematics (ISM) identified the need for professional development to address thedemands of this new era as a key goal of its second medium-term plan (financial year
2010–2015). Under the ISM’s Network of Excellence (NOE) initiative, we are pursuing
big-data-related R&D utilizing a wide range of tools, including machine learning, data
assimilation, risk analysis, and next-generation survey methods. We are also striving
to foster young data scientists, through various professional development programs at
the ISM’s School of Statistical Thinking, which serves as our principal base for education and training programs in statistical thinking.
The “Coop with Math Program”, launched in November 2012, is a project that ISM isundertaking on contract for the Ministry of Education, Culture, Sports, Science andTechnology (MEXT). Under the program, we are devising ways to promote research
to stimulate innovation and creativity through collaboration between mathematics/
mathematical science and various other sciences and industries, and big data is certainly an important focus of research in this initiative. Selected to serve as the core institution of this project, ISM is actively collaborating with eight major Japanese centersof research and education in mathematics and mathematical science as part of theinitiative. As a research institute, ISM is wholeheartedly committed to fundamental
research in fields related to data, with a view to fulfilling the expectations of society.
In this effort, we look forward to your continued understanding and support.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of
the modeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, thedepartment aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, which function effectively interms of predicting phenomena or scientific discoveries, through data analysis and modeling related to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Data Design Group ——————————————————————————————————————————————————The Data Design Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of survey and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms in statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain phenomena,
stochastic processes and their application to inference, probability and distribution theory, and the related mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis, optimization,
discrete mathematics, and control and systems theory for computation-based statistical inference as well as their applications.

NOE-type Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the uncertainty and risks in society,which have increased with the growing globalization of society and the economy. The Center is also constructing
a network for risk analysis with the goal of contributing to creating a safe and resilient society.
2Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to produce designs for effective observation systems by means of “data assimilation”, which is afundamental technology integrating numerical simulations and observational data.

Survey Science Center
Founded on the accomplishments in social research by the Institute of Statistical Mathematics spanning overhalf a century including the Study of the Japanese National Character and the cross-national comparative researchon national characteristics, the Survey Science Center was established in January of 2011 in order to facilitate
further growth of the aforementioned sets of research as well as the establishment of networking ties with both
domestic and international research organizations and the increase in the capacity to make contributions to wider
society by creating what we call the NOE (Network Of Excellence).

Research Center for Statistical Machine Learning
Machine learning is a research field studying autonomous systems that learn from data. This field is based on
the statistical science that concerns inference from data and the computer science that studies algorithms. The
application of machine learning is broad, ranging from engineering, including robotics, and information sciences
to natural science, such as brain science. This research center aims at supporting the academic community of the
field, in addition to producing influential works via various joint projects.

Service Science Research Center
The aim of the newly established Service Science Research Center is to apply data-centric methodologies tothe service fields — from marketing, supply chain management, and management engineering, to the modelingof social systems. We will integrate the insights from these fields to establish data-centric service sciences as acommon discipline, through collaborations with universities and institutions worldwide, taking advantage of ISM’s
Network Of Excellence (NOE) program.

Professional Development
School of Statistical Thinking
The School of Statistical Thinking was established as a center for the planning and implementation of various
programs for professional development and education and training in statistical thinking. In the setting of a joint
research facility, the school is working to develop professionals (specialists with broad knowledge and skills,
modelers, research coordinators, etc.) equipped with the statistical thinking ability to meet the demands of the “bigdata era”, in which large-scale data sets are utilized for modeling, research coordination, and other applications.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, software for research, networking infrastructureand network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining an extensive library and an electronic repository, and is incharge of planning statistical education courses to popularize research results.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of the publication and editing of research results and is responsible for public relations.

3Research Centers
Risk Analysis Research Center

The Practical Research Project for Financial Big Databases andfor Credit Risk Validation Models
N Formulating business needs and developing

N Modeling and developing credit risk databaseresearch network in financial risk society
It is major social interest to accurately estimate
risk of credit administration and market investment.
Our project develops the methodological solutions
for this problem by statistical approach and prob­
ability theory. We manage the series of operations
such as model building, system development andback testing valuation, using actual credit database.
We plan research exchange between practicians
and academicians in order to define paractician’s
problem and to supply its solutions to them.

We develop a credit risk validation model using
marginal database of banks and Credit Risk Database
Guarantee Association. This model has practical performance to be implemented in relation to Basel II
and International Financial Reporting Standards.
New statistical method which reflects the characterof financial data is promoted for missing value imputation and outlier correction when marginal databaseis developed. We confirm that KNN (k-nearest neighborhood) method is more efficient than empiricalmodification such as ACE (Imputation by Chained
Equations) or imputation by preceding period data.
Satoshi Yamashita

N Evaluation and selection for models

of financial risk
Recent risk valuation models of credit risk
and market risk are complicated due to big
database and complex financial commodities. Therefore, it is important for the model
user to select highly accurate model to beconsistent with his purpose. Then, valuation
measure of risk model is significantly needed. Our project contributes the valuation
methods for financial institution and financial database company by considering a lotof risk measure on their perspective. For example, the estimation model of probability ofcorporate rejuvenation and the research for
repayment capacity of corporate on default
status are supplied to them to valuate accurate financial credit risk. We are promoting
the project by collaborative alliance to banks.

Figure 1: The earthquake time-space influence to credit risk by themarginal database.

Imputation by KNN method

Figure 2: Conceptual diagram of kNN method for financial time series data.

4

The Effort of the Statistical Science towards Solution ofEnvironmental Problems
N Mission of the project

N Statistical analysis of air radiation dose rates

A risk analysis research center / environmental
statistics research project aims at performing thecontribution towards solution of the environmental
problem which is a modern subject by developing
the optimal new statistical methodology for eachenvironmental problem (for example, the problem
of dioxin, the problem of global warming, the problem of continuous use of safe water). Moreover, inorder to realize this purpose, in cooperation withthe community of environmental science, researchis carried out including a visiting teacher or a project
researcher.

and degree of radio-contamination of soil
in Fukushima
East Japan major earthquake struck East Japanon March 11, 2011. Subsequently, tsunami attacked
Fukushima Daiichi Power Plot. As a result, in all of
1-3 Nuclear reactors, meltdown befell. Then, significant amounts of radioactive material have been released into environment. The Ministry of Education
conducted a survey of the soil radioactive contamination and air radiation dose rate neighborhoods
Fukushima Daiichi nuclear power plant in June-July,
2011. We analyzed the association between the air
dose rate and the soil radiation contamination using
the data. When there was precipitation at the measurement date, the coefficient of determination has
become a 64%. The determination coefficient reduced to 29% for a day with no precipitation. According to this analysis, about 20% to 30% of air radiation dose rate cannot be explained by the degree ofsoil radioactive contamination directly beneath. It
was suggested that local decontamination may
have a limited effect on reducing the air radiationdose rate.
Koji Kanefuji

N Directional statistics in environmental science

It is possible to model the vanishing angles of migratory birds and the orientations of logs on the floorusing distributions on the circle. Wind direction is atypical angular variable in environmental science,and it is important to use information on wind direction for a better understanding of, for example, radiation dose and ozone concentration. An angle has a
special feature different from a linear variable inmathematics. We study statistical modeling andanalysis of data which include angular observationsin the framework of directional statistics.

Figure 1: An example of circular distribution.

Figure 2: Observed spatial distribution of degree of cesiumcontamination ( logarithmic value of Bq/m2 ) of soil in
Fukushima.

5Research and Development Center for Data Assimilation

Data Assimilation for Solid Earth Science
N Learning from the 2011 Great East Japan

Earthquake
Although the Great East Japan Earthquake on March
11, 2011 was one of oceanic interplate earthquakes,
which are rather predictable due to their periodicity,
the magnitude of approaching tsunamis was underestimated even by numerical simulations using massively
parallel computers, global-scale observation networks,
and the Earthquake Early Warning System, which makes
full advantages of IT. Later research has clarified thatobservation data actually contained much information
indicating the tsunamis, therefore simulation models inwhich such information was properly embedded would
have accurately predicted their magnitude. Harmonious
integration of simulations and observations throughdata assimilation (DA) is important in anticipation of theforthcoming Tokai-Tonankai-Nankai Earthquake.
N Seismic phenomena recorded in non-seismic

observations

tsunami early warning system utilizing such seismoacoustic waves, which propagate much faster than tsunamis.
N Data assimilation system for seismoacoustic waves
Our DA system has successfully reproduced the seismoacoustic wave propagation due to the Iwate-Miyagi
Nairiku Earthquake and the ionospheric oscillation due
to the Great East Japan Earthquake utilizing ISM Supercomputing System for Statistical Science (Fig. 1). We implemented a DA procedure on a parallel computer to estimate source parameters rapidly and accurately frommicrobarometer data at Sugadaira and Shionomisaki.
The system simulates many scenarios of seismoacoustic
wave propagations based on hypocenter solutions, andestimates probability density functions of the parameters by assimilating the observation data in conjunction
with the Markov chain Monte Carlo method (Fig. 2). We
are currently concentrating on speeding up the DA process and the observation data handling with the aim ofrealizing a practical system that can rapidly compute themagnitude and arrival time of an approaching tsunami,
which is predicted to arrive within a few minutes in thecase of the expected Tokai-Tonankai-Nankai Earthquake.
Hiromichi Nagao

Recent elaborated Earth observation sensors enable
us to detect seismic phenomena even from non-seismic
observations. For instance, DA research on GPS data hasrevealed that two oceanic plates are firmly
bonded in “asperities” and slippery in otherregions. Accumulated energy due to plate subduction may be released at once in asperities
as great earthquakes. Another example is thatmicrobarometers often detect “sound of earthquake”, which is acoustic wave that is emitted
from the hypocenter and propagated in theupper atmosphere. Seismoacoustic waves
excited by tsunamis of the Great East Japan
Earthquake traveled several thousands of kiFigure 1: (Left ) Propagation of seismoacoustic waves excited by the 2008
lometers, oscillating the ionosphere at an alIwate-Miyagi Nairiku Earthquake, and (right) ionospheric oscillation due
titude of several hundreds of kilometers.
to the seismoacoustic waves excited by the 2011 Great East Japan
Earthquake, both of which were reproduced by data assimilation.
This phenomenon motivates us to establish a

Figure 2: (Top) Workflow in the data assimilation system for seismoacoustic waves, and (bottom) its validation based on a twin experimentassuming seismoacoustic waves excited by the Tonankai Earthquake, which is predicted to take place in the near future.

6

Research Centers

Simulation-based Design of Intervention Programme against Influenza
N Continuous appearance of new influenza strain

New influenza strains appear continuously via
mutations and genetic reassortments. The worldwide spread of infection, namely pandemic, occurs
because the most of people do not have immunity
against such a virus. The so-called swine flu in 2009is one of such pandemics. Fortunately, this strain is
low pathogenic and the symptom is comparable tothat of seasonal flu. However, human infection casesof an avian flu strain with an extremely high mortality
rate are reported in the end in the 90’s. The threat
comparable to a Spanish flu in 1918 would be brought,
if this strain evolved to acquire a steady infectivity
to human beings.
N Infection spread forecast by simulation

The prediction of epidemics by the simulation is important to design effective intervention programmes.
After 2000, as parallel computing is getting popular,
agent-based epidemic simulations become realistic
approach. In an agent-based simulation, some modelcity, consisting of households, schools, workplaces
and so on, is constructed in computer and infectioustransmissions are carried out stochastically insidetemporal and local groups formed as result of individual activities. This “direct approach” enables us
to incorporate the effect of individual interventions

like school closures in a selected local region naturally into simulations.
N Vaccine vaccination plan to maximize herd

immunity
We studied how degree the collected immunity isenhanced by optimising the order of vaccination
among social groups. It should be impossible toprepare an enough stockpile of vaccines for all theresidents and it is necessary to set priorities. In thepresent policy, vaccines are distributed in the following order: medical practitioners, high risk persons (including elderly persons) and miscellaneous.
This policy is based on the idea that students andoffice workers can endure the disease. However, itis necessary to give priority to students and the office workers, who have a large contact frequency,from the viewpoint of enhancing the collective immunity. Actually, our simulations show that if students and employees are dosed for the first one
month, the illness attack ratio in the non-vaccinatedgroup is decreased below the half, whereas this ratio is almost changeless, if non-workers are targeted. In addition, the former vaccination plan reduces also the mortality rate in all age groups andhence it can be say that the collective immunity isimproved without sacrificing high risk persons.
Masaya M. Saito

Figure : (Top) The schematic illustration of our simulator, (Bottom) Infectious spread over a model city.

7

Survey Science Center

Research on Survey Methodology and Planning of Cooperative
Training Survey
N Process of social survey and researchon survey methodology
The Institute of Statistical Mathematics (ISM) has
been conducting many social surveys for almost sixty years. One purpose of these surveys is to study
public opinions in Japan. Another important purpose
is to study survey methodology in real-life settings.
Social surveys mainly consist of two processes, as
is depicted in figure 1. One is the process of sampling(right hand side), where representativeness of thesample is the main concern. The other is the process
of measurement (left hand side), where we try to capture the opinion of respondents exactly. Each step in
Figure 1 has its own methodological subjects to bestudied.

and Linguistics (NINJAL) called “Fourth Survey of Language Standardization in Tsuruoka”. This is a cooperative survey project between ISM and NINJAL since1950, and the survey in Tsuruoka-city, Yamagata Prefecture, has been repeated at about twenty-year intervals. The design of this survey is a combination of arepeated cross-sectional survey and panel survey as
is depicted in Figure 2.
We are planning to continue this kind of survey practice for the coming years, with the support of visiting
professors from outside the institute. The goal of thisactivity is to broaden the base of research on survey
methodology by fostering more survey professionals.
Tadahiko Maeda

N Practice of survey research by ISM

Formerly surveys of ISM were administeredby ourselves with supports from many universities, employing students as part-time interviewers. But during the past two decades thistradition has been lost, entrusting most of thesurvey operation to survey company. Worsening
survey circumstances in the present society do
not allow us to ask non-professional interviewers to complete our survey. But this also meantthat we have gradually lost opportunities forresearch on survey methodology.
N Start-ups of the collaborative training

surveyMore recently, social science departments ofmany universities have undergone curriculum
reformation which places more emphasis on
survey practice. In line with this trend, Survey
Science Center has begun to administer what
we call “Collaborative Training Survey”, underclose cooperation with university departments
and other survey-related institutions. Thus, wewill offer faculty members and students morechances to participate in large-scale surveys.
As a start-up, we conducted a nationwide
survey on “Social Stratification and Social Psychology” (SSP survey) in 2010 with Osaka University. In 2011 we carried out a joint survey
with National Institute for Japanese Language
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Figure 1: Process of social survey and subject of research on survey
methodology.

Figure 2: Design of Survey of Language Standardization in Tsuruoka,
Sixty years of Cooperative Project with NINJAL.
“Random” refers to a survey on a sample randomly selected,“Panel” refers to a panel survey on the same informants.
Numbers in the rectangle indicate sample size.

Research Centers

Building a Database of Social Survey Results
N Objective of the social survey information

research project
The Social Survey Information Research Projectat the Survey Science Center has been collecting social survey results and building a social survey database so that anyone can browse the survey results
easily on the Web. A single survey provides limited
information, particularly in the recent deteriorated
survey environment. Multidimensional analysis using various types of social surveys is essential forcomprehending the complicated aspects of modern
society. This research project studies the methodologies for building a database and develops statistical methods for analyzing data. The group intends
to make the database available for public use.
N A comprehensive report of a survey on theJapanese national character
The core data used to develop the database is obtained from a survey on the Japanese national character, which is conducted on a regular basis by theInstitute of Statistical Mathematics. This survey
has been conducted every five years since the first
1953 survey. The next thirteenth survey will be conducted in the autumn of 2013. The questionnaire
items in the survey concerned the attitudes of theparticipants toward life, environment, religion, politics, race, the Japanese people, and so forth. The
results of this longitudinal survey, which are crucial
for evincing the transition of Japanese character,are already available on the Internet. More comprehensive results, including cross-tabulation tables
and graphs, are also provided on the Internet.
N Database of related surveys

The survey on the Japanese national character is
by no means the only survey conducted by the Institute. Various interrelated comparative surveys
and experimental surveys have also been conducted. One of them is public opinion survey in Tokyo
conducted since 1953 to 1982. Another is residents
survey conducted in Tama area, which has been
started after the removal of the Institute to Tachikawa city in 2009. The results of these surveys arealso included in the database and made accessible
to the public one at a time. The potential users of
this database, including not only researchers but
also the general public, will be able to analyze thedata from various perspectives. The visualization
software developed in this project is provided forother institutions, whose results are also linked
from the project web page.
Takahiro Tsuchiya

Figure: Browsing survey
results on the web.

9

Research Center for Statistical Machine Learning

Statistical Analysis for English Mental Lexicons
N Abstract of the researchThis research began when Prof. Mitsuru Orita ofUniv. Kumamoto, a researcher on English learning,
asked me how to analyze the data of experiments
on English native speakers and English-learners
and evaluate the difference of their mental lexicons
statistically.
The data is collected by asking two groups of examinees, thirty English native speakers and thirty
English-learners, to classify fifty English words. We
do the same examinations for various kinds of English words, for example, with different frequencies ofappearance and different word classes and analyze
the difference of their results.
In this research, we assume that the structure ofeach mental lexicon is a dendrogram as in Fig.1.
Thus, our target is the evaluation of the difference
between two dendrograms.
N Permutation test for dendrograms

In order to compare the two dendrograms, we proposed a data analysis by a permutation test which

generates many random dendrograms under a hypothesis of the equivalence of mental lexicons of
the two groups and compare them with the dendrograms of the original data. Fig. 2 is a summary of the
permutation test.
The method proposed here is completely novel, so
we need to justify it by theory. Therefore we proved
the consistency of the permutation test under anassumption of local linearity on the algorithm forconstructing dendrograms. The assumption holds,
fortunately, for most of the ordinary algorithms used
for making dendrograms.
N Theory and application of simplicial fansStatistical data used for permutation tests have asimple structure usually. However the set of dendrograms has a complicated geometrical structure
called a simplicial fan (Fig. 3). While general simplicial fans are too complicated to be analyzed, thesimplicial fan of dendrograms has a good mathematical property called CAT(0). By using the property,
we proposed a permutation test by using the geodesic distance.
N Further target

In addition to the examinations explained
above, we have done various kinds of examinations, for example, for elementary level
learners or with translated words. We expect
such results can be used for making efficient
tools to study English and have already begun
a group research project on that purpose withsome specialists of English learning.
Kei Kobayashi
Figure 1: Dendrograms for adjectives( top: Japanese, bottom: native speakers).

Figure 2: Sketch of the permutation test for dendrograms.

10

Figure 3: A simplicial fan of dendrograms.

Research Centers

Reading the Neural CodeN Motivation

N Summary of recent work

Perception, memory, learning and decision-making
are processes carried out in the brain. The performance of such intelligent tasks is made possible by
the communication of neurons (the cells of which
the brain consists) through sequences of voltage
pulses called “spikes.” I am interested in formulating
methods of extracting information from the neuronal activity exhibited while the brain carries out
high-level processes. For this purpose, I have beenworking on constructing methods for analyzing thestatistics of neuronal spikes.

Neural activity in cortex exhibits variability (Figure 1), the characteristics of which are specific toeach neuron and are strongly correlated with thefunction of the cortical area. However, its neuralcoding advantages, or limitations posed by thisvariability are not completely understood. In a recent study, we address a question of how the information about variation of firing rate, carried by
spike trains, is related quantitatively to the variability of neuronal firing.
For this purpose, we introduce the relative entropy as a measure of information on variable firingrate. We then give an explicit interpretation of thisinformation in terms of detectability of rate fluctuation: the lower bound of detectable rate fluctuation,
below which the temporal variation of firing rate isundetectable with a Bayesian decoder, is entirely
determined by this information (Figure 2). We showthat the information depends not only of the variation of firing rates (i.e., signals), but also significantly on the dispersion properties of firing describedby the shape of the interspike interval distribution(i.e., noise properties).
With the basis of our theoretical results, we aregoing to analyze real data, to estimate how muchinformation biological spike trains contains.
Shinsuke Koyama

Figure 1: (Top) raster plot of spike trains.
(Bottom) an estimated firing rate.

Figure 2: The lower bound below which the underlying rate variation is not detectable (solid line).

11Service Science Research Center

Statistical Methods for Large-scale Data in Service Industries
N Statistical data analysis in service industries
According to the rapid development of computer
technology including the Internet, huge amount ofdata are generated and stored continuously in service
industries like POS data. We have to analyze suchlarge-scale data to clarify the data generation process
and to acquire useful knowledge for future activity.
For solving those problems, we focus on interactive
data visualization and symbolic data analysis (SDA).
N Aggregation and interactive visualization oflarge-scale data
When we first face complicated large-scale data
generated in service industries, data visualization
techniques utilizing the recent computer technology
is helpful to extract the hidden useful features in it,
Figure 1 is an example of visualizing POS data in a certain convenience store chain by using several linked
spineplots. A spineplot is a variation of barchart.
In these graphics, data for the customers whose

Figure 1: An example of visualization of POS data in a certain
convenience store chain.

ages are 20s and 30s are selected and highlighted byan interactive operation (see the area “age”). Corresponding data in other spineplots are also highlighted
at the same time. These graphics show several usefulcharacteristics of data. For example, proportions ofthese customers are higher in nighttime than in daytime (see the area “time”).
Figure 2 is generated in the process of analyzing Web
log data produced in a real-estate searching website.
Two barcharts illustrate numbers of real-estate properties in special wards in Tokyo metropolitan municipal district and suburban Tama area. In these graphics,
the distribution of real-estate properties is clearlyshown by aggregating huge amount of original data.
In this way, visualization software which can aggregate and operate large-scale data interactively andeasily is indispensable to clarify features of complicated huge amount of data.
N Development of statistical methodologies foranalyzing aggregated large-scale data
For analyzing large-scale data of service industries,
we are often interested in groups which consist of naturally and meaningfully similar individuals. Suchgroups are considered as “concepts” in symbolic data
analysis (SDA). In SDA, “concepts” are expressed bysymbolic data (SD) such as interval, histogram, etc.
SDA provides techniques for analyzing such SD by extending existing analysis techniques, such as principal
component analysis, clustering, and multidimensional
scaling. In these techniques, groups are described by
the information about marginal distribution. We noticethat the information based on marginal distribution
is not sufficient for several analyses. Thus, we usesome information of joint distribution in a group for
analyzing groups. For this purpose, we propose theconcept of aggregated symbolic data (ASD) which use
more information thanthe marginal distribution, for example, covariance information
among variables, andstudy several required
analysis techniques for
ASD.
Nobuo Shimizu

Figure 2: An example of visualization of distribution for real-estate properties in Tokyo metropolitan area.

12Research Centers

Data Analysis Improving the Satisfaction of Individual Consumer
N Modeling for information extraction of individual

Recently, a new topic of study, called service science, has been attracting attention because decision
making regarding services that had conventionallybeen carried out based on experience or perception
have come to be performed scientifically. As one of
the main themes in service science, we can cite consumer heterogeneity. Companies have become able
to obtain data at an individual level; however, it isstill necessary to model the overall structure in order to extract information on individual consumers
because there are few data about each individual.
On the other hand, because of the progress of sensor
technologies, companies have become able to obtainlarge-scale and detailed data about consumers. At
this center, by fusing the approaches that have originally developed in marketing science, service engineering, artificial intelligence, and statistical science,
we study data analytical techniques to improve thesatisfaction of individual consumers. We introduce
two study examples in the following.
N Optimization of internet ad delivery system

Because of the progress of Internet technologies,
this way of thinking about advertisement is changing. In the new Internet ad delivery system called
real-time bidding (RTB), which has been rapidly

Figure 1: Conceptual Diagram of RTB.

spreading recently, the ad shown is determined byan auction. By means of this system, companies havebecome able to deliver an ad to only specific users
based on their browse history. The effect of an adlargely depends on the users who see it, so it is crucial to understand to whom a company should show
an ad. At this center, we are developing a system foreffective ad delivery.
N Movie recommendation by a service

engineering approach
There are many products and services aroundus, and anyone is now able to obtain information
about them. Therefore, determining what items acompany shows to customers has become a very
important task. We developed an algorithm thatrecommends the best movies to each user by using
probabilistic latent semantic analysis (PLSA), which
was originally proposed as a way to estimate themeaning of documents from word matrices in natural language processing. Specifically, we assume theexistence of common latent classes among users
and items and that each item and user stochasticallybelongs to each class. The algorithm enables highprecision prediction even for users whose browse
history is not sufficiently recorded by analyzing theattribution of the users belonging to the class.
Tomoyuki Higuchi

Figure 2: Graphical Model of PLSA.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Biaxial Structure
The Institute of Statistical Mathematics pursues research and education along the two lines of basic research and NOE (Network Of Excellence)-type research,(we explain about “NOE” later), conducted by the basic
research departments along a horizontal axis and theNOE-type research centers along a vertical axis (Figure1). By its nature, the basic research departments cut
across and link various disciplines, developing tools forinterdisciplinary research. The field of statistical mathematics must itself evolve to meet the changing needs ofsociety and the data environment, and is therefore constantly evolving as a field of study. At the same time, there
are approaches and directions that remain unchanged
as the field evolves. For that reason, we have chosen not
to call it fundamental research or foundational research
but “basic research”, to reflect both the fixed and evolving qualities of statistical mathematics. There are threebasic research departments: statistical modeling, data
science, and mathematical analysis and statistical inference. These departments engage in cutting-edge research to develop methodologies for rational prediction
and decision making, based on data and existing knowledge. Each of the Institute’s permanent researchers is
assigned to one of these basic research departments.

N Research Facilities
The Institute’s research facilities comprise the NOE(Network Of Excellence)-type research centers andthe school for developing professionals. The NOE-typeresearch centers (vertical axis) are staffed by permanent
researchers within the Institute, project researchers(post-doctoral staff), and visiting professors and researchers. The current NOE-type research centers are
the Risk Analysis Research Center, Research and Devel-

Biaxial
Structure

Figure 1: Biaxial Structure for research and education

14

School of Statistical Thinking

Service Science

Statistical Machine Learning

Survey Science

Mathematical
Analysis andStatistical Inference

Data Assimilation

Data Science

Development( Top-down )Risk Analysis(Bottom-up )Basic Research

Statistical
Modeling

NOE-type Research Professional

opment Center for Data Assimilation, Survey Science
Center, Research Center for Statistical Machine Learning,
and Service Science Research Center. These centers conduct research activities that interface statistical mathematics with individual scientific disciplines in order tofind solutions to urgent social problems. In addition,these centers promote the NOE activities as the cores ineach NOE domain.
We launched the School of Statistical Thinking to foster and promote “statistical thinking”. In the School of
Statistical Thinking, researchers and students who havean eye on the creation of a new statistical research fieldand various people who realize the necessity of statistics in their particular research field train together tofoster statistical thinking. For that, we have changed
the personnel system to post newly employed assistantprofessors at the School of Statistical Thinking.

N NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan of
the Research Organization of Information and Systems,
the Institute of Statistical Mathematics has set as a goal
the establishment of NOE (Network Of Excellence) in
statistical mathematics. We have been establishing NOEin the five research areas of integrated risk research,next-generation simulation, survey science, statisticalmachine learning, and service science. The NOE Projectpursues activities to establish new methodologies intheir respective research fields and serve as hubs forinterdisciplinary interaction. We operate an NOE Promotion Unit to strengthen our coordination skills and to
facilitate the overall advancement of the NOE Project,crucially supporting the goal of erecting a fourth scientific methodology in our knowledge society that extends
beyond resolving individual problems. The Advisory

Senior Advisor, RIKEN Brain Science
Institute

Dr. Shun-ichi Amari

Adjunct Professor, Graduate School of
System Information, Kobe University

Dr. Yoshio Oyanagi

President, Shiga University

Dr. Takamitsu Sawa

Professor Emeritus, Tokyo University

Dr. Motoyuki Suzuki

President, Center for International
Public Policy Studies

Prof. Naoki Tanaka

President, The Japan Pharmaceutical
Manufactures Association

Dr. Isao Teshirogi

Professor, Sociology and Research
Methodology School of Cultural andCreative Studies, Aoyama Gakuin University

Dr. Kazufumi Manabe

Director-General, Center for Research
Development and Strategy,
Japan Science and Technology Agency

Dr. Hiroyuki Yoshikawa

Director-General, Institute for
Monetary and Economic Studies

Prof. Tomoo Yoshida

Table 1: Advisory Board of the NOE ProjectBoard of the NOE Project, which is organized as experts
in the private sector, academia, and the government(Table 1), advises our Managing Committee of the NOE
Project and each NOE-type research center so that we canformulate a unified management strategy for the project.
On September 24th, 2012, we held the Advisory Board
meeting of the NOE Project in order to explain our past
NOE activities and to collect opinions from the members of the Advisory Board for further research activities in the next stage of the NOE Project. (Photo 1)
The Institute has created a new logo in light of thecontinued growth of research under the NOE. The logo
(Figure 2) symbolizes the advancement of comprehensive research through the combined and cumulative efforts of the five NOE, alongside the research efforts intheir respective research areas. The logo is used for theactivities of the NOE Project at the Institute and at our
related organizations.

Figure 2: logo

Photo 1: Advisory Board meeting on Sept. 24th, 2012

focusing all activities on advancing research in each of
the research fields of risk research, next-generation simulation, survey science, statistical machine learning, andservice science. Also, we plan to unite the five NOE mentioned above with the NOE Research Promotion Organization. Using this NOE Research Promotion Organization,
the Institute will aim to develop new research areas andestablish new collaborative research systems (Figure3). To learn more about the project, please visit thewebsite of NOE (http://noe.ism.ac.jp/english/)

N Expansion of Activities
by NOE ( Network Of Excellence)
The Institute is actively pursuing agreements withother research organizations in order to expand the roleof the five NOE-type research centers as core researchhubs. In the 2012-2013 Japanese academic year, we havesigned new Memorandum of Agreements with two Japanese institutes and three foreign organizations.
From a methodological perspective, our Institute is

Risk Research NOE

Advice from
Advisory Board

Dept. of Risk Engineering, Graduate
School of Systems and Information
Engineering, Univ. of Tsukuba

Organization
Cooperation in ISM

Jul. 2008

Related by MOA

Future related
research organizations

Graduate School ofLife Science,
Tohoku Univ.
Mar. 2011

Centers whoserole is the“core” of NOE

The Dept. of Ecoinformatics
Biometrics and Forest Growth of the
Georg-August Univ. of Goettingen

Relatedorganizations

Oct. 2012

Dept. of Probability andMathematical Statistics of theCharles Univ. in Prague

44 organizations in Risk
Research Network

New Relatedorganizations
signed MOA
in 2012-2013

Oct. 2012

Graduate School of Economics
and Management, Tohoku Univ.
Jul.2012

Graduate School of Business
Sciences, Univ. of Tsukuba
Dec.2012

The Institute of Statistical
Mathematics
Research andService Science

Research Center

Future related
research organizations

Managing Committeeof NOE Project

NOE Promotion
Unit
Dept. of Empirical Inference of Max-Planck
Institute for Biological Cybernetics(Germany) / Aug. 2010

The Human Language Technology Dept.
Institute of Infocomm Research(Singapore)(Germany) //Aug.
Jan. 20102012

Center for Computational Statistical
Machine Learning, Univ. College London

Next-Generation
Simulation NOE

Risk Analysis
Research Center

Service Science
NOE

Development Center
for Data Assimilation

Advisory Board
of NOE Project(Norway) / Jan. 2012

Feb. 2007

Institute of Fluid Science,
Tohoku Univ.
Jul. 2010

Solar-Terrestrial Environment
Laboratory, Nagoya Univ.
Sep. 2010

Future related
research organizations

NOE Research Promotion Organization
Research Center for
Statistical Machine Learning

Survey Science
Center(GBT) / Feb. 2012

Dept. of Communication Systems, SINTEF
Information and Communication Technology

RIKEN Computational Science
Research Program

Dept.of Electronics andTelecommunication, Norway Univ.
of Science and Technology (NTNU)(Norway) / Jan. 2012

Graduate School of Human Science,
Osaka Univ.
Jul. 2010

National Institute for Japanese
Language and Linguistics
Oct. 2012

Future related
research organizations

Statistical Machine Learning NOE

Future related
research organizations

Survey Science NOE

Figure 3: Relationship Diagram of Network Of Excellence15Project for Fostering and Promoting Statistical Thinking

Because of the “data explosion” in modern society, people who can think and handle such data statistically and retrieve useful information are greatly required in all fields of science and society. The
Japanese education system unfortunately has fallen short in producing a sufficient number of people
who have useful statistical thinking abilities. We established the School of Statistical Thinking in ISM to
improve this situation. We hope that our activities, including tutorial courses, seminars, and cooperative
research projects, are useful for developing professionals with the proper knowledge and skills to tackle
complicated problems involving huge amounts of data.

Research Collaboration Start-up
The Institute had been providing a consultation service on statistical science. Along with the launchof the School of Statistical Thinking in November 2011, this service was reorganized as a research collaboration start-up. This program, being one of the projects to foster and promote statistical thinking, ismainly aimed at supporting applied scientists and other related people. Expert statisticians affiliatedwith the Institute give scientists and others advice on statistical modeling, data analysis, and research.
Some cases develop into official research collaborations, which are our primary duty as an interuniversity research institute. The start-up program covers a variety of topics, ranging from introductory tomore specialized studies. Half of the clients are from the private sector, and the rest are staff at publicorganizations, university professors, or students. The Institute accepts about 20 cases annually, some ofwhich benefit society in diverse ways.

Open-type Human Resource Development Program
In order to foster and promote “statistical thinking”, the Institute of Statistical Mathematics invites
proposals of research projects and workshops to develop young researchers. In FY 2013, two projects
and four workshops have been adopted after review.

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on
statistical mathematics every Wednesday. The
seminars are led by in-house and external lecturers to showcase their latest studies. These lectures
are free to attend. To view the seminar scheduleand learn more about the program, please visit theInstitute of Statistical Mathematics website.
http://www.ism.ac.jp/

16Open Lecture
We hold an open lecture during Education and Culture Week every year (November 1 through 7),
to introduce the Institute’s activities and to promote statistical science. We invite lecturers to speak
on a timely topic relating to statistical science. The lecture is open to the general public. For further
information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/

Tutorial Courses■ History
The Statistical Education Program started in 1944, the year that the Institute of Statistical Mathematics was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located within the Institute.
In 1947, the affiliated Statistical Technician Training Center was opened as an educational organization
for statistical technicians and instructors.
As social needs have changed, the purpose of the education program has gradually shifted away fromsupplying statistical technicians for the government, towards educating working people. Tutorial courses were therefore initiated.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation, and the affiliated Statistical Technician Training Center was abolished. However, in response toconsistent public demand for tutorial courses, the Center for Engineering and Technical Support, together with other departments, ran three to four courses annually. In 2005, the number of courses rose
to 13. Since April 2012, the School of Statistical Thinking, which the Institute launched in November 2011
in order to foster and promote statistical thinking, has been running tutorial courses. In 2012, the number of courses was 11.

■ Courses
The total number of courses held from 1969 to March, 2013 was 293, with a total of 21,363 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past year:
Year

Category

Title

Month

Number ofparticipants

2012Standard course

Statistical Analysis by Information Criteria

April

90

Advanced course

An Introduction to Statistical Analysis by the Theory of Martingales

June

38

Basic course

Basic Course of Statistics

July - August

98

Basic course

Introduction to Multivariate Analysis

August95

Basic course

Analysis of Sample Surveys with R

September

38

Standard course

An Introduction to Statistical Graphical Models

November

38

Standard course

Statistical Analysis of Forest Growth Data and Its Applications

December

26Standard course

Statistical Methods for Missing Data

December

99Standard Advanced course

Introduction to Statistical Topic Models

January69

Advanced course

Bayesian Data Analysis; Case Examples

February

20

Standard course

Theory and Practice of Information Processing Based on Sparsity,—Compressed Sensing and Related Topics—

March

65

2013

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address
USA ( Washington)

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census
Stichting Mathematisch Centrum

The Kingdom of theNetherlands (Amsterdam)

May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University
Institute for Statistics and Econometrics, Humboldt University of Berlin
Institute of Statistical Science, Academia Sinica
The Steklov Mathematical InstituteCentral South University
Soongsil University
Department of Statistics, University of Warwick
The Indian Statistical InstituteDepartment of Empirical Inference, Max Planck Institute for Biological Cybernetics
Faculty of Medicine, University of São Paulo
Deparztment of Communication Systems, SINTEF Information and Communication Technology
Human Language Technology Department Institute for Infocomm ResearchCentre for Computational Statistics and Machine Learning, University College London
Department of Electronics and Telecommunications, Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics, Charles University in Prague
The Department of Ecoinformatics, Biometrics and Forest Growth of the Georg-August University of Goettingen

The Republic of Korea (Seoul )Germany (Berlin)
Taiwan ( Taipei )Russia (Moscow)
China (Changsha)
The Republic of Korea (Seoul )The United Kingdom (Coventry)
India (Kolkata )Germany ( Tubingen)
Brasil (São Paulo)
Norway ( Trondheim)
Singapore (Singapore)
The United Kingdom (London)

October 17, 2002 December 8, 2004 June 30, 2005 August 9, 2005 November 18, 2005 April 27, 2006 January 16, 2007 October 11, 2007 August 11, 2010 April 15, 2011January 30, 2012February 16, 2012February 16, 2012May 22, 2012October 10, 2012October 18, 2012-

Norway (Trondheim)
Czech Republic (Prague)
Germany (Goettingen)

July 27, 1988 -Research Collaboration
This academic study program provides research­ers from other academic institutes with access to the facilitiesof the Institute, and provides opportunities for researchers to conduct theoretical and applied studies on statistics.

■ Number of Activities
2007

2008

2009

2010

2011

2012

120

138

154

135

172

182

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
Numberabcde

FieldsNumberSpatial and time series modelingIntelligent information processing
Graph modeling
Survey researchMultidimensional data analysis

f
g
h
i
j

FieldsComputational statistics
Mathematical statistics
Learning and inference
Computational mathematics
OthersMajor Research Fields
Number

1
2
3
4
5
6
7
8

18

FieldsMajor Research Domains

Statistical mathematics
Information science
Biological science
Physical science
Engineering
Human science
Social science
OthersMathematical theory of statistics, optimization, etc.
Algorithms, use of computer in statistics, etc.
Medicine, pharmacy, epidemiology, genetics, etc.
Space, planet, earth, polar region, materials, etc.
Mechanics, electronics, control, chemistry, architecture, etc.
Philosophy, art, psychology, education, history, geography, culture, language, etc.
Economics, law, politics, society, management, official statistics, population, etc.
Other research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education
and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

President

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes designing of data-gathering systems, modeling, inference and forecasting in order to extract information and knowledge from the real world based on empirical data, as well as basic,mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

19

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science byprofessors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of

the best in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible

for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the course are as follows:

Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research

results.

Number of Students ( As of April 1, 2013)■ Doctor’s course five years
Year of enrollment

2008 2009

Number of students1

1 (1 )■ Doctor’s course three years
2010

2012

2013

Year of enrollment

2007

2008 2009

2010

2011

2012

2013

2

2

2

Number of students

1

2

3

7

3

3

2

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University (1 ) • Tohoku University ( 2 ) • Fukushima University (1 ) • University of Tsukuba (6) • Saitama University (1 )• Ochanomizu University (1 ) • Hitotsubashi University ( 6 ) • Chiba University (1 ) • The University of Tokyo (17) • Tokyo Medical and
Dental University (1 ) • Tokyo Gakugei University (1 ) • Tokyo Institute of Technology ( 4 ) • Tokyo University of Marine Science andTechnology (1 ) • Tokyo University of Agriculture and Technology (1 ) • Tokyo Metropolitan University (1 ) • Shizuoka University (1 )• Japan Advanced Institute of Science and Technology, Hokuriku (1 ) • Nagoya University ( 3 ) • Toyohashi University of Technology ( 2 )• Kyoto University ( 4 ) • Osaka University ( 2 ) • Osaka City University (1 ) • Nara Institute of Science and Technology (1 ) • Okayama
University ( 2 ) • Shimane University ( 3 ) • Kyushu University ( 2 ) • Oita University (1 )20Graduate School Program

University Background of Students
Private universities• Okayama University of Science (1 ) • Tokyo University of Science ( 6 ) • Kyoto Sangyo University (1 ) • Keio University ( 6 ) • Waseda
University ( 8 ) • Chuo University ( 7 ) • Toyo University (1 ) • Nihon University ( 2 ) • Hosei University ( 7 ) • Kurume University (1 )• Japan Women’s University (1 ) • Shibaura Institute of Technology (1 ) • Nanzan University(1)Foreign universities• Aston University (1 ) • University of California, Irvine (1 ) • Universidade Eatadual of Campians (1 ) • University of Colorado at
Boulder ( 2 ) • University of Dhaka ( 2 ) • University of Hawaii (1 ) • Jahangirnagar University ( 2 ) • University of Malaya (1 ) • Ohio
University (1 ) • University of Rahshahi ( 2 ) • Stanford University (1 ) • Center for Analysis and Prediction, China Seismological
Bureau (1 ) • Northeastern University of Technology, China (1 ) • The Hong Kong University of Science and Technology (1 )• University of Science and Technology of China (1 ) • Chinese Academy of Sciences, Institute of Applied Mathematics (1 )Degrees Awarded
Year

2007

2008

2009

Doctor of Philosophy

7 [1]

4 [1]

5 [1]

2010

2011

2012

7 [1]

4

6 [1]

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Instituteof Technology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National
University • Hokkaido University • Tokyo Institute of Technology • Hiroshima University • JAXA’s Engineering Digital Innovation
Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation• Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis • Government Pension
Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Science • Shibaura Institute ofTechnology • Rikkyo University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co., Ltd. • Bridgestone
Corporation

21

Facilities and Equipment

Computational Resources
Since January 2010, the Supercomputer System
for Statistical Science has been in operation andhas analyzed a large volume of statistical data.
The main components are a shared memory system (Fujitsu SPARC Enterprise M9000 with 2
nodes, the first with 64 quad-core SPARC64 CPUs
and 2 TB of main memory, and the second with
24 quad-core SPARC64 CPUs and 1 TB of main memory), and a distributed memory system (Fujitsu
PRIMERGY RX200S5 with 360 nodes, each with 2
quad-core Xeon X5570 CPUs and 48 or 24 GB ofmain memory. In total, 2880 cores and 12.1 TB ofmemory are available). The system also includes
a large-scale shared storage system (1.37 PB disk
storage supported by RAID6), a physical random
number generating system (two random number
generator boards, each of which can generate random numbers at 400 MB/s) and a visualization
system (including a SXRD projector with a maximum resolution of 4,096 × 2,160 and a 200-inch rearprojection screen).
In the office building, the primary Local Area Network (LAN) consists of an Ethernet network using
10GBASE-SR for the main trunk and 1000Base-T
for branches. The personal computers in researchers’ offices, the Supercomputer System for Statistical

Science are all connected to this network. A wireless LAN system that supports IEEE 802.11a,b,g,n
is also available in the area of the building occupied by the institute. These LAN systems enabledistributed processing and computing resources
and statistical data to be used effectively. Comprehensive network security measures have been
implemented such as a firewall system, anti-virus
software, and an intrusion prevention system. To
encourage joint research with researchers bothin Japan and abroad, as well as the exchange ofe-mails, the network is connected to the Internet
through SINET3 (1 Gbps).

Supercomputer System for Statistical Science

Library and Materials ( As of April 1, 2013)
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that
we publish ourselves: Annals of the Institute of Sta­tistical Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics(Japanese), ISM Survey Research Report (Statistical Researches mainly related to the Japanese
National Character), Computer Science
Monographs, Cooperative Research Re­
ports(for collaborative research proJapanese Foreign
periodicals periodicals
jects), Research Memorandum, ISM Reports on Statistical Computing, and ISM
1,018
1,179
Report on Research and Education.
Periodicals 54%
46%
2,197
All materials are properly catalogued
and can be searched from the web inorder to meet the needs of research­ers
working in a wide of fields. We also accept photocopy requests.

We have a large number of major Japanese/foreign journals covering a wide variety of fields including statistics, mathematics, computer scienceand informatics. In addition, we also have a large
library consisting of books on humanities, social
science, biology, medical science, science and engineering.

Japanesebooks
16,98027%

Books
62,814Foreign books
45,83473%

22

Finance and Buildings

Administration Subsidy and Others ( 2012)Type

Personnel expenses

Non-personnel expenses

Total

690,464

938,298

1,628,762

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2012)Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

11

7

4

22

Income70,193

6,392

4,600

81,185
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2012)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research ( S )1

34,190Grant-in-Aid for Scientific Research ( A)

556,940Grant-in-Aid for Scientific Research ( B)424,180Grant-in-Aid for Scientific Research ( C)

15

19,500Grant-in-Aid for Challenging Exploratory Research

5

6,240

Grant-in-Aid for Young Scientists ( B )1111,960Grant-in-Aid for Research Activity Start-up

1

1,170

Grant-in-Aid for JSPS Fellows

2

2,000

44

156,180

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2013)
Site Area

62,450m 2

Area for Buildings (total)

16,026m 2

23

Organization

Organization Diagram ( As of August 1, 2013)■ Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Latent Structure Modeling Group■ Data Design Group

Department of Data Science■ Metric Science Group■ Structure Exploration GroupCouncil

Scientific
Advisory
Board■ Complex System Modeling Group

Department of Mathematical
Analysis and Statistical Inference■ Mathematical Statistics Group■ Learning and Inference Group■ Computational Inference Group

Risk Analysis Research Center
Research and Development
Center for Data Assimilation

DirectorGeneral

Survey Science Center
Research Center for
Statistical Machine Learning

Vice DirectorGeneral

Service Science ResearchCenterSchool of Statistical Thinking

Center for Engineering andTechnical Support■ Computing Facilities Unit
■ Information Resources Unit
■ Media Development Unit

Library

Planning Unit
Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Promotion Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM
Joint Administration Office

The Institute of
Statistical Mathematics

Planning Section ( ISM )General Service Center24

Number of Staff ( As of April 1, 2013 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Professor Professor rative Staff
Staff

1

Total

1

Department of Statistical Modeling

5

7

2

14

Department of Data Science

5

7

4

16

Department of Mathematical Analysis and Statistical Inference

6

5

4

15

Center for Engineering and Technical Support

1010

Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

11 ( 29)

1 ( 2)

12 ( 31)12 ( 29)

11 ( 2 )

69 ( 31)

Total

1

16

19
(

10

1

) Total number of staff of NIPR/ISM Joint Administration Office.

The number under Technical Staff at the Center for Engineering and Technical Support includes
one staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2013 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Hiroshi MARUYAMA

Department of Statistical Modeling
Director Tomoko MATSUI• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————
Prof.

Nobuhisa KASHIWAGIProf.

Tomoyuki HIGUCHI

Assoc. Prof.

Genta UENO

Assist. Prof.

Shinya NAKANOAssoc. Prof.

Jiancang ZHUANG• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Junji NAKANOAssoc. Prof.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assist. Prof.

Shinsuke KOYAMA

Visiting Prof.

Takehiro FUKUIVisiting Prof.

Hiroko NAKANISHI

Visiting Assoc. Prof. Kazuhiro AOYAMA• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Hiroshi MARUYAMAProf.

Tomoko MATSUIAssoc. Prof.

Ryo YOSHIDA

Project Assist. Prof. Sayaka SHIOTA

Assoc. Prof.

Yoshinori KAWASAKI

Department of Data Science
Director Takashi NAKAMURA• Data Design Group ———————————————————————————————————————————————————————————————————
Prof.

Takashi NAKAMURA

Prof.

Ryozo YOSHINO

Assoc. Prof.

Naomasa MARUYAMA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Toshihiko KAWAMURA

Visiting Prof.

Kazufumi MANABE

25

Department of Data Science• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Assoc. Prof.

Kenichiro SHIMATANI

Assist. Prof.

Nobuo SHIMIZU

Assist. Prof.

Hisashi NOMA

Assoc. Prof.

Masayuki HENMI• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Hiroe TSUBAKI

Prof.

Koji KANEFUJI

Assoc. Prof.

Manabu KUROKI

Assist. Prof.

Yoo Sung PARK

Assoc. Prof.

Jun ADACHI

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Shuhei MANO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Shogo KATO

Assist. Prof.

Kei KOBAYASHI• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Project Assist. Prof. Osamu KOMORI• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Project Assist. Prof. Tadayoshi FUSHIKI

Risk Analysis Research Center
Director Satoshi YAMASHITA
Prof.

Hiroe TSUBAKI

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Nobuhisa KASHIWAGI

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Manabu KUROKI

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Yoshinori KAWASAKI

Assoc. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Hironori FUJISAWA

Assoc. Prof.

Jun ADACHIAssist. Prof.

Takaaki SHIMURA

Assist. Prof.

Shogo KATO

Assist. Prof.

Hisashi NOMA

Project Assoc. Prof. Takaki IWATA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Toshiya SATO

Visiting Prof.

Yoichi KATO

Visiting Prof.

Masaaki MATSUURA

Visiting Prof.

Hirohisa KISHINO

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Kunio SHIMIZU

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Megu OTAKI

Visiting Prof.

Tetsuji IMANAKA

Visiting Prof.

Nobuo YOSHIDA

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Tadashi KITAMURA

Visiting Prof.

Katsuhiko TAKATA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Hidetoshi SHIMODAIRA

Visiting Prof.

Satoru ENDO

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Hsien-kuei HWANG

Visiting Assoc. Prof. Hideki KATAGIRI

26

Vice Director Masayuki HENMI

Visiting Assoc. Prof. Koji OKUHARA

Project Assist. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Hisateru TACHIMORI

Organization

Staff
Risk Analysis Research Center
Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Toshikazu KITANO

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Toshio ONISHI

Visiting Assoc. Prof. Jun OHASHI

Visiting Assoc. Prof. Toshihiro HORIGUCHI

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Yoshiyuki NINOMIYA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Katsuya TANAKA

Visiting Assoc. Prof. Masakazu ANDO

Visiting Assoc. Prof. Yasutaka SHIMIZU

Visiting Assoc. Prof. Masaaki FUKASAWA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Takahiro YONEZAWA

Visiting Assoc. Prof. Seisho SATO

Project Researcher

Takao KUMAZAWA

Research and Development Center for Data Assimilation
Director Tomoyuki HIGUCHI

Vice Director Yoshiyasu TAMURA

Prof.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOAssoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Yukito IBA

Assist. Prof.

Shinya NAKANO

Project Assoc. Prof. Hiromichi NAGAO

Project Assist. Prof. Christopher Andrew ZAPART

Project Assist. Prof. Masaya SAITO

Project Assist. Prof. Terumasa TOKUNAGA

Visiting Prof.

Takashi WASHIO

Visiting Assoc. Prof. Kazuyuki NAKAMURA

Project Researcher

Kazue SUZUKI

Survey Science Center
Director Ryozo YOSHINOProf.

Ryozo YOSHINOProf.

Takashi NAKAMURA

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Takahiro TSUCHIYA

Assist. Prof.

Yoo Sung PARK

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Hidekazu CHAYAMA

Visiting Prof.

Fumi HAYASHI

Visiting Prof.

Masato YONEDA

Visiting Assoc. Prof. Toru KIKKAWA

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Koken OZAKI

Project Researcher

Project Researcher

Kosuke NIKAIDO

Visiting Assoc. Prof. Wataru MATSUMOTO
Project Researcher

Kiyohisa SHIBAI

Ryoji MATSUOKA

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU

Vice Director Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITO

Assoc. Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Assist. Prof.

Kei KOBAYASHI

Assist. Prof.

Shinsuke KOYAMA

Visiting. Prof.

Atsuko IKEGAMI

Visiting. Prof.

Takashi TSUCHIYA

Visiting. Prof.

Masataka GOTO

Visiting. Prof.

Koji TSUDA

Project Assist. Prof. Yu NISHIYAMA
Visiting. Prof.

Tadashi WADAYAMA

Visiting Assoc. Prof. Yuji SHINANO

Visiting Assoc. Prof. Ido NEVAT

Visiting Assoc. Prof. Arthur GRETTON

Service Science Research Center
Director Hiroshi MARUYAMAProf.

Hiroshi MARUYAMAProf.

Tomoyuki HIGUCHIProf.

Hiroe TSUBAKI

Prof.

Tomoko MATSUIProf.

Junji NAKANOAssoc. Prof.

Manabu KUROKI

Assist. Prof.

Toshihiko KAWAMURA

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Yoshiki YAMAGATA

27

Service Science Research Center
Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Tadahiko SATO

Visiting Assoc. Prof. Haruhisa FUKUDA

Project Researcher

Visiting Assoc. Prof. Yukihiko OKADA

Hisanao TAKAHASHI

School of Statistical Thinking
Director Junji NAKANO

Vice Director Yoshinori KAWASAKIProf.

Hiroshi MARUYAMAProf.

Satoshi ITO

Assoc. Prof.

Yukito IBA

Assoc. Prof.

Naomasa MARUYAMA

Adjunct Professor

Masami HASEGAWA

Adjunct Professor

Yasumasa BABA

Adjunct Professor

Makio ISHIGURO

Project Assist. Prof. Kei TAKAHASHI

Project Researcher

Shigeru FUJITA

Project Assist. Prof. Keiichi FUKAYA

Project Researchers
Project Assoc. Prof. Kazuhiro MINAMI
Project Researcher

Project Assoc. Prof. Roberto Sebastian LEGASPIMotoi OKAMOTO

Project Researcher

Satoko SAITA

Project Assist. Prof. Kazuhiko SHIBUYA
Project Researcher

Xiaoling DOU

Center for Engineering and Technical Support
Director

Junji NAKANO

Deputy Manager

Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Akiko NAGASHIMA

Vice Director
Senior Specialist

Yoshinori KAWASAKI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Junji NAKANO

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHI28

Head of Planning Unit

Hiroe TSUBAKI

Head of Evaluation Unit

Yoshiyasu TAMURA

Head of Information and Public Relations Unit

Hiroshi MARUYAMAHead of Intellectual Property Unit

Hiroshi MARUYAMAHead of NOE Promotion Unit

Hiroe TSUBAKI

Visiting Prof.

Takashi NAMESHIDA

Organization

Staff
NIPR/ISM Joint Administration OfficeDirector of NIPR/ISM Joint Administration Office

Tsugio TOKUTA

Director of General Service Center

Tomoyoshi SHIBUSAWA• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Kenichi TADA
Deputy Head

Mitsuaki OGAWA

Deputy Head

Kazuhiko GOTO

Specialist

Fumio SUTOTeam Leader

Mitsuo ENDOTeam Leader

Hiroaki ARAI

Team Leader

Tomoko ODA• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Michiaki NAKANO
Deputy Head

Yasuyuki EDURETeam Leader

Kazuhisa OSHITATeam Leader

Norihito SETOTeam Leader

Yoji ISHIISpecialist

Hiroyasu KUMAGAISpecialist

Tomoya MORITA• General Service Center —————————————————————————————————————————————————————————————————————
Deputy Head

Koji SAKAMOTO

Deputy Head

Takashi KUBOTA

Deputy HeadTomohiko MIYAUCHITeam Leader

Yumiko OKAWA

Team Leader

Kazuyoshi ASO

Team Leader

Kenichi SHIOBARA

Specialist

Yoshihiro YAMADASpecialist

Michihito SAKURAI

Council of The Institute of Statistical Mathematics ( As of April 1, 2013)
Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering , Tokyo Institute of Technology

Masanori IYEProfessor, Optical and Inferred Astronomy Division, National Astronomical Observatory of Japan, National Institutesof Natural Sciences

Koji KURIHARA

Professor, Okayama University Executive Vice Director / The Graduate School of Environmental and Life Science

Kunio SHIMIZU

Professor, Faculty of Science and Technology, Keio University

Nobuhiko TERUIProfessor, Graduate School of Economics and Management, Tohoku University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yoshihiro YAJIMA

Professor, Graduate School of Economics, The University of Tokyo

Shoichi YOKOYAMA

Professor, Department of Linguistic Theory and Structure, National Institute for Japanese Language and Linguistics

Takashi WASHIO

Professor, The Institute for Scientific and Industrial Research, Osaka University

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Hiroe TSUBAKI

Professor (Vice Director-General, ISM )Yoshiyasu TAMURA

Professor ( Vice Director-General, ISM )Hiroshi MARUYAMA

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM )Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANOProfessor (Director of Center for Engineering and Technical Support, ISM )Shinto EGUCHI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Nobuhisa KASHIWAGI

Professor (Department of Statistical Modeling, ISM )Ryozo YOSHINO

Professor (Department of Data Science, ISM )29

Organization

Cooperative Research Committee ( As of April 1, 2013)
Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Yoshinori FUJII

Professor, Faculty of Education and Culture, University of Miyazaki

Shigeyuki MATSUI

Professor, Graduate School of Medicine, Nagoya University

Hiroshi YADOHISA

Professor, Faculty of Culture and Information Science, Doshisha University

Kazue YAMAOKA

Professor, Graduate School of Public Health, Teikyo University

Tomoko MATSUI

Professor (Director of Department of Statistical Modeling, ISM )Satoshi YAMASHITA

Professor (Department of Data Science, ISM )Satoshi ITOProfessor (Department of Mathematical Analysis and Statistical Inference, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Research Ethics Review Committee ( As of April 1, 2013)
Specialist on epidemiology and social research

Kazuo SEIYAMA

Professor, Kwansei Gakuin University

Specialist on epidemiology and social research

Keiko SATO

Associate professor, Kyoto Unit Center Japan Environment &Children’s Study, Graduate School of Medicine, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Principal, Tachikawa City Daiichi Elementary School

Research education staff of ISM

Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM)

Research education staff of ISM

Hiroe TSUBAKI

Professor ( Vice Director-General, ISM)

Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis andStatistical Inference, ISM )Research education staff of ISM

Tadahiko MAEDA

Associate Professor (Department of Data Science, ISM )Professor Emeritus ( As of April 1, 2013)30

Kameo MATUSITA

Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

History

1944

June ● Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed
to provide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

1947

April ● The affiliated statistical specialists’ school was opened.
May ● The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept.
(statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories for thesocial sciences).

1949

June ● The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

1955 September ● Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural and social
science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories). The
laboratory system, comprising 9 laboratories and the research guidance promotion room, was adopted.
1969

October ● A new office building was constructed in Minato Ward.

1971

April ● The 4th Research Dept. (informatics theories) was instituted.

1973

April ● The 5th Research Dept. (prediction and control theories) was instituted.

1975

October ● The 6th Research Dept. (statistical theories of human behavior) was instituted.

1979 November ● The Information Research Building was constructed.
1985

April ● Repositioned as an Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on statistical mathematics. Accordingly, the institute was reorganized into our basic research
departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics) and two strategic centers (Statistical Data Analysis Center and Statistical Education & Information Center). The Statistical Technical Training Center was terminated.

1988

October ● The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies ( SOKENDAI ).

1989

June ● The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

1993

April ● The Planning Coordination Chief System was instituted.

1997

April ● The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

2003 September ● The Prediction and Knowledge Discovery Research Center was instituted.
2004

April ● The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National
University Corporation Law. The Planning Coordination Chief System was abolished and the positionof Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­
matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Scienceand the School of Multidisciplinary Sciences were instituted.

2005

April ● The research organization was reorganized into three research departments ( the Department of Statis­
tical Modeling, the Department of Data Science, and the Department of Mathematical Analysis andStatistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for
Information on Statistical Sciences, and the Engineering and Technical Services Section were inte­
grated into the Center for Engineering and Technical Support. The Risk Analysis Research Center was
instituted.

2008

April ● The Research Innovation Center was instituted.
The Intellectual Property Unit was instituted.

2009
2010

October ● The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.
June ● Officially opened the Akaike Guest House.
July ● Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch
the General Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within the Administration Planning and Coordination Section.

2011

January ● Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January ● Research Center for Statistical Machine Learning, Service Science Research Center and School of
Statistical Thinking were instituted.

31

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/